Detection and prevalence of carbapenem-resistant Gram-negative bacteria among European laboratories in the COMBACTE network: a COMBACTE LAB-Net survey
Bibliographic record
Abstract
Antimicrobial resistance (AMR) represents a global public health threat that jeopardises the progress medicine has made over the last century. To confront AMR, the Innovative Medicines Initiative (IMI) has supported the development of a large network of hospitals and laboratories in Europe as part of the New Drugs for Bad Bugs (ND4BB) programme and the COMBACTE projects. COMBACTE LAB-Net conducted a pilot survey on distribution and usage of carbapenem resistance detection methods among laboratories in the COMBACTE network in two clinical trials as part of the COMBACTE-CARE project. The survey was sent out to 211 laboratories in 20 European countries between May 2015 and June 2017. Answers were collected from 165 laboratories (78%). Sixty laboratories (36%) reported an outbreak of carbapenem-resistant (CR) Enterobacteriaceae during one of the two years preceding the completion of the survey. High rates of CR Acinetobacter spp. above 50% were reported by 74 laboratories (47%), particularly in the Western Balkan countries where the rates were sometimes higher than 90%. Apart from determining the antimicrobial susceptibility of isolates, laboratories also used various methods, such as Matrix Assisted Laser Desorption Ionization - Time of Flight (MALDI-TOF), Carbapenemase Nordmann-Poirel (Carba NP) test or molecular methods, to detect CR Gram-negative bacteria. The survey resulted in the selection of sites with high resistance rates that successfully recruited many patients in the EURECA observational clinical trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".